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LLMs solve hard math problems via symbol manipulation without true understanding

By PulseAugur Editorial · [1 sources] ·

Large Language Models (LLMs) can solve complex mathematical problems by treating mathematics as a rule-based symbol manipulation task. This approach allows LLMs to generate solutions, such as Python code, without necessarily understanding the underlying mathematical concepts. The process relies on training the models on extensive mathematical texts to learn these symbolic rules. AI

IMPACT This perspective suggests LLMs may excel at formal tasks by mimicking rule-based systems, even without deep comprehension.

RANK_REASON The item discusses a conceptual understanding of how LLMs approach mathematical problem-solving, rather than announcing a new model or research finding.

Read on Mastodon — fosstodon.org →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LLMs solve hard math problems via symbol manipulation without true understanding

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5 / 100
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Commentary
The item discusses a conceptual understanding of how LLMs approach mathematical problem-solving, rather than announcing a new model or research finding.
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High
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Breaking (< 6h)
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COVERAGE [1]

  1. Mastodon — fosstodon.org TIER_1 English(EN) · [email protected] ·

    I'm tentatively understanding the news about LLMs offering barely comprehensible solutions to hundreds of hard maths problems in the following way. # Maths is s

    I'm tentatively understanding the news about LLMs offering barely comprehensible solutions to hundreds of hard maths problems in the following way. # Maths is symbol manipulation according to set rules. You can do this task by training an LLM on a lot of maths writing. You don't …

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